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Advanced manufacturing includes not only the assembly of products on the shop floor but also a wide variety of complex processes involving quality control, predictive maintenance, resource management, controlling, and customer engagement. The use of big data analytics in manufacturing can lead to new insights and knowledge discovery that can help increase productivity, improve quality, and reduce costs. This has already been demonstrated by a few early success stories that are now appearing in the popular media. For instance, using big data analytics, Bosch, was able to increase yield and reduce scrap produced, while Intel was able to significantly reduce the number of tests required for quality assurance.
While there has been a plethora of research and publications in traditional domains such as recommendation systems, social media, and health care, there is a marked absence of a special interest research group for big data analytics and data-mining in the non-traditional domains such as manufacturing. This workshop aims to fill this gap. Specifically, this workshop aims to bring together leading practitioners from the industry, academia, and government to collaborate and nurture manufacturing innovation using big data. We expect this workshop will help connect modern data science techniques with manufacturing engineering and contribute to the development of advanced manufacturing technology worldwide. It will comprise talks and posters from the aforementioned groups and a panel discussion on the big data opportunities in advanced manufacturing.
This workshop provides a platform to discuss and focuses on the needs and opportunities in advanced manufacturing.
Research topics of interest include (but are not limited to):
A co-located workshop http://www.simtech.a-star.edu.sg/events/ieee-workshop-on-big-data-analytics-in-manufacturing-and-supply-chains.aspx deals with supply chain management and logistics in manufacturing and the special session http://cci.drexel.edu/bigdata/bigdata2015/#SpecialSession1 focuses on implementation and standards in manufacturing.
This workshop accepts both short (upto 4 page IEEE 2-column format) and long papers (upto 10 page IEEE 2-column format). Submissions can be done through the online submission system. Your paper should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (http://www.ieee.org/conferences_events/conferences/publishing/templates.html). Paper should be in the PDF format.